sciphi 0.1.3

SciPhi: A Framework for LLM Powered Data.

SciPhi [ΨΦ]: A Framework for LLM Powered Data

SciPhi is a Python-based framework designed to facilitate the generation of high-quality synthetic data tailored for both Large Language Models (LLMs) and human users. This suite offers:

• Configurable Data Generation: Craft datasets mediated by LLMs according to your specifications.
• Retriever-Augmented Generation (RAG) Integration: Make use of an integrated RAG Provider API to ground your generated data to real-world datasets. Also, SciPhi comes bundled with an evaluation harness to optimise your RAG workflow.
• Textbook Generation Module: A module to power the generation of RAG-augmented synthetic textbooks straight from a given table of contents.

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Fast Setup

pip install sciphi

Optional Dependencies

Install with specific optional support using extras:

• Anthropic: 'sciphi[anthropic_support]'
• HF (includes Torch): 'sciphi[hf_support]'
• Llama-CPP: 'sciphi[llama_cpp_support]'
• Llama-Index: 'sciphi[llama_index_support]'
• VLLM (includes Torch): 'sciphi[vllm_support]'
• All (no vLLM): 'sciphi[all]'
• All (with extras, e.g. vLLM): 'sciphi[all_with_extras]'

Recommended (All Optional Dependencies):

pip install 'sciphi[all_with_extras]'

Setup Your Environment:

Navigate to a working directory and use a text editor to adjust the .env file with your specific configurations.

# Proprietary Providers
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
# Open Source Providers
HF_TOKEN=your_huggingface_token
# vLLM
VLLM_API_KEY=your_vllm_token
# RAG Provider Settings
RAG_API_BASE=your_rag_server_base_url
RAG_API_KEY=your_rag_server_key

After entering your settings, ensure you save and exit the file.

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Features

Community & Support

• Engage with our vibrant community on Discord.
• For tailored inquiries or feedback, please email us.

Textbook Generation

This is an effort to democratize access to top-tier textbooks. This can readily be extended to other domains, such as internal commercial documents.

Generating Textbooks

1. Dry Run:

   python -m sciphi.scripts.textbook_generator dry_run --toc_dir=sciphi/data/sample/table_of_contents --rag-enabled=False
   

   This will perform a dry-run over the default textbooks stored in sciphi/data/sample/textbooks.

   Note - this must be run from the root of the repository, or else you will need to update the toc_dir accordingly. Setting rag-enabled to True will enable RAG augmentation during the generation process. You may customize the RAG provider through additional arguments.

2. Textbook Generation:

   python -m sciphi.scripts.textbook_generator run --toc_dir=sciphi/data/sample/table_of_contents --rag-enabled=False
   

   Replace dry_run above with run to generate one textbook for each table of contents in the target directory. See a sample textbook here.

3. Example With a Custom Table of Contents:

   Prepare your table of contents and save it into $PWD/toc/test.yaml. Then, run the following command:

   python -m sciphi.scripts.generate_textbook run --toc_dir=toc --output_dir=books --data_dir=$PWD
   

   For help with formatting your table of contents, see here.

4. Custom Settings & RAG Functionality:

   Simply switch rag-enabled to True. Ensure you have the right .env variables set up, or provide CLI values for rag_api_base and rag_api_key.

   Alternatively, you may provide your own custom settings in a YAML file. See the default settings configuration here.

Important: To make the most out of grounding your data with Wikipedia, ensure your system matches our detailed specifications. An example RAG provider can be seen here. More high quality outbook books are available here.

RAG Eval Harness

To measure the efficacy of your RAG pipeline, we provide a unique RAG evaluation harness.

Running the RAG Harness

python -m sciphi.scripts.rag_harness --n-samples=100 --rag-enabled=True --evals_to_run="science_multiple_choice"

This example runs over 100 science multiple choice questions with RAG enabled and reports the final accuracy.

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Local Development

Local setup

1. Clone the Repository:

   Begin by cloning the repository and stepping into the project directory:

   git clone https://github.com/emrgnt-cmplxty/sciphi.git
   cd sciphi
   

2. Install the Dependencies:

   Start by installing the primary requirements:

   pip install -r requirements.txt
   

   If you require further functionalities, consider the following:

   • For the developer's toolkit and utilities:

     pip install -r requirements_dev.txt
     

   • To encompass all optional dependencies:

     pip install -r requirements_all.txt
     

   Alternatively, to manage packages using Poetry:

   poetry install
   

   And for optional dependencies w/ Poetry:

   poetry install -E [all, all_with_extras]
   

3. Example - Create your own LLM and RAG provider:

   from sciphi.core import LLMProviderName, RAGProviderName
   from sciphi.interface import LLMInterfaceManager, RAGInterfaceManager
   
       llm_interface = LLMInterfaceManager.get_interface_from_args(
           provider_name=LLMProviderName(llm_provider_name),
           model_name=llm_model_name,
           # Additional args
           max_tokens_to_sample=llm_max_tokens_to_sample,
           temperature=llm_temperature,
           top_k=llm_top_k,
           # Used for re-routing requests to a remote vLLM server
           server_base=kwargs.get("llm_server_base", None),
       )
   
       rag_interface = (
           RAGInterfaceManager.get_interface_from_args(
               provider_name=RAGProviderName(rag_provider_name),
               base=rag_api_base or os.environ.get("RAG_API_BASE"),
               token=rag_api_key or os.environ.get("RAG_API_KEY"),
               max_context=rag_max_context,
               top_k=rag_top_k,
           )
           if rag_enabled
           else None
       )
      # ... Continue ...
   

   Supported LLM providers include OpenAI, Anthropic, HuggingFace, and vLLM. For RAG database access, configure your own, or get access to the SciPhi gigaRAG API.

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System Requirements

Essential Packages:

• Python Version: >=3.9,<3.12

• Required Libraries:

  • bs4: ^0.0.1
  • fire: ^0.5.0
  • openai: 0.27.8
  • pandas: ^2.1.0
  • python-dotenv: ^1.0.0
  • pyyaml: ^6.0.1
  • retrying: ^1.3.4
  • sentencepiece: ^0.1.99
  • torch: ^2.1.0
  • tiktoken: ^0.5.1
  • tqdm: ^4.66.1

Supplementary Packages:

• Anthropic Integration:
  • anthropic: ^0.3.10
• Hugging Face Tools:
  • accelerate: ^0.23.0
  • datasets: ^2.14.5
  • transformers: ^4.33.1
• Llama-Index:
  • llama-index: ^0.8.29.post1
• Llama-CPP:
  • llama-cpp-python: ^0.2.11
• VLLM Tools:
  • vllm: 0.2.0

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Licensing and Acknowledgment

This project is licensed under the Apache-2.0 License.

Citing Our Work

If SciPhi plays a role in your research, we kindly ask you to acknowledge us with the following citation:

@software{SciPhi,
author = {Colegrove, Owen},
doi = {Pending},
month = {09},
title = {{SciPhi: A Framework for LLM Powered Data}},
url = {https://github.com/sciphi-ai/sciphi},
year = {2023}
}